基于VGG-M网络模型的前方车辆跟踪*

doi:10.19562/j.chinasae.qcgc.2019.01.009

Abstract

Abstract: Aiming at the low accuracy of front moving vehicle tracking in complex scenes, the huge VGG-M network model is applied to real-time tracking, and the online observation model is used to achieve stable and accurate tracking of front vehicles. By improving the sample generation scheme and optimizing the network training set, the efficiency of network training is enhanced. Furthermore, with adaptive update model adopted, the network update frequency can be adjusted in real time according to the aspect ratio of target profile, internal information entropy and the confidence of tracking scale. Experimental results show that the online VGG-M tracking model achieves better performance than the traditional vehicle tracking methods.

Key words: deep learning, front vehicle tracking, online observation model, network adaptive update model

Liu Guohui, Zhang Weiwei, Wu Xuncheng, Song Xiaolin, Xu Sha, Wen Peigang. Front Vehicle Tracking Based on VGG-M Network Model[J]., 2019, 41(1): 57-63.

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Front Vehicle Tracking Based on VGG-M Network Model

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